1
|
He K, Wan D, Li S, Yuan G, Gao M, Han Y, Li Z, Hu D, Meng X, Niu Y. Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy. Pediatr Nephrol 2024; 39:1447-1457. [PMID: 38041747 DOI: 10.1007/s00467-023-06224-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS. METHODS Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2 weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF. RESULTS SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r = - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all). CONCLUSIONS NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.
Collapse
Affiliation(s)
- Kangwen He
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Dongyi Wan
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shichao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Guanjie Yuan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mengmeng Gao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunfeng Han
- Department of Nuclear Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Daoyu Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaoyan Meng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Yonghua Niu
- Department of Pediatric Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| |
Collapse
|
2
|
Zhao D, Wang W, Tang T, Zhang YY, Yu C. Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review. Comput Struct Biotechnol J 2023; 21:3315-3326. [PMID: 37333860 PMCID: PMC10275698 DOI: 10.1016/j.csbj.2023.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023] Open
Abstract
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
Collapse
Affiliation(s)
- Dan Zhao
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Wei Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Tian Tang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Ying-Ying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| |
Collapse
|
3
|
Zhu L, Huang R, Zhou Z, Fan Q, Yan J, Wan X, Zhao X, He Y, Dong F. Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features. ULTRASONIC IMAGING 2023; 45:85-96. [PMID: 36932907 DOI: 10.1177/01617346231162910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Kidney transplantation is the most effective treatment for advanced chronic kidney disease (CKD). If the prognosis of transplantation can be predicted early after transplantation, it might improve the long-term survival of patients with transplanted kidneys. Currently, studies on the assessment and prediction of renal function by radiomics are limited. Therefore, the present study aimed to explore the value of ultrasound (US)-based imaging and radiomics features, combined with clinical features to develop and validate the models for predicting transplanted kidney function after 1 year (TKF-1Y) using different machine learning algorithms. A total of 189 patients were included and classified into the abnormal TKF-1Y group, and the normal TKF-1Y group based on their estimated glomerular filtration rate (eGFR) levels 1 year after transplantation. The radiomics features were derived from the US images of each case. Three machine learning methods were employed to establish different models for predicting TKF-1Y using selected clinical and US imaging as well as radiomics features from the training set. Two US imaging, four clinical, and six radiomics features were selected. Then, the clinical (including clinical and US image features), radiomics, and combined models were developed. The area under the curves (AUCs) of the models was 0.62 to 0.82 within the test set. Combined models showed statistically higher AUCs than the radiomics models (all p-values <.05). The prediction performance of different models was not significantly affected by the different machine learning algorithms (all p-values >.05). In conclusion, US imaging features combined with clinical features could predict TKF-1Y and yield an incremental value over radiomics features. A model integrating all available features may further improve the predictive efficacy. Different machine learning algorithms may not have a significant impact on the predictive performance of the model.
Collapse
Affiliation(s)
- Lili Zhu
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Renjun Huang
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou City, Jiangsu Province, P.R. China
| | - Qingmin Fan
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Junchen Yan
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Xiaojing Wan
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Xiaojun Zhao
- Department of Urology, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Yao He
- Suzhou Key Laboratory of Nanotechnology and Biomedicine, Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou City, Jiangsu Province, P.R. China
| | - Fenglin Dong
- Department of Ultrasound, the First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, P.R. China
| |
Collapse
|
4
|
Mo X, Chen W, Chen S, Chen Z, Guo Y, Chen Y, Wu X, Zhang L, Chen Q, Jin Z, Li M, Chen L, You J, Xiong Z, Zhang B, Zhang S. MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study. Insights Imaging 2023; 14:28. [PMID: 36746892 PMCID: PMC9902579 DOI: 10.1186/s13244-023-01370-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 01/03/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models. RESULTS The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively. CONCLUSION We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.
Collapse
Affiliation(s)
- Xiaokai Mo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Wenbo Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China ,grid.470066.3Department of Radiology, Huizhou Municipal Central Hospital, No. 41 Eling Bei Road, Huizhou, 516001 Guangdong People’s Republic of China
| | - Simin Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhuozhi Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yuanshu Guo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yulian Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Xuewei Wu
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Lu Zhang
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Qiuying Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhe Jin
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Minmin Li
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Luyan Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Jingjing You
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhiyuan Xiong
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
| |
Collapse
|
5
|
Chen W, Zhang L, Cai G, Zhang B, Lian Z, Li J, Wang W, Zhang Y, Mo X. Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study. Front Endocrinol (Lausanne) 2023; 14:1050078. [PMID: 37139339 PMCID: PMC10150993 DOI: 10.3389/fendo.2023.1050078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/28/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction Diabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN. Methods For this retrospective study, 70 patients (between 1 January 2013 and 1 January 2020) were included and randomly assigned to the training cohort (n1 = 49) and the testing cohort (n2 = 21). According to the estimated glomerular filtration rate (eGFR), patients were assigned into the normal renal function (normal-RF) group, the non-severe renal function impairment (non-sRI) group, and the severe renal function impairment (sRI) group. Based on the largest coronal image of T2WI, the speeded up robust features (SURF) algorithm was used for texture feature extraction. Analysis of variance (ANOVA) and relief and recursive feature elimination (RFE) were applied to select the important features and then support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms were used for the model construction. The values of area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis were used to assess their performance. The robust T2WI model was selected to construct a multimodal MRI model by combining the measured BOLD (blood oxygenation level-dependent) and diffusion-weighted imaging (DWI) values. Results The mMRI-TA model achieved robust and excellent performance in classifying the sRI group, non-sRI group, and normal-RF group, with an AUC of 0.978 (95% confidence interval [CI]: 0.963, 0.993), 0.852 (95% CI: 0.798, 0.902), and 0.972 (95% CI: 0.995, 1.000), respectively, in the training cohort and 0.961 (95% CI: 0.853, 1.000), 0.809 (95% CI: 0.600, 0.980), and 0.850 (95% CI: 0.638, 0.988), respectively, in the testing cohort. Discussion The model built from multimodal MRI on DN outperformed other models in assessing renal function and fibrosis. Compared to the single T2WI sequence, mMRI-TA can improve the performance in assessing renal function.
Collapse
Affiliation(s)
- Wenbo Chen
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
- Department of Radiology, Huizhou Central People’s Hospital, Huizhou, Guangdong, China
| | - Lu Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Guanhui Cai
- Department of Radiology, Huizhou Central People’s Hospital, Huizhou, Guangdong, China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Zhouyang Lian
- Department of Radiology, Guandong Academy of Medical Sciences/Guangdong Provincial People’s Hospital, Guangzhou, Guangdong, China
| | - Jing Li
- Division of Nephrology, Guangdong Academy of Medical Sciences/Guangdong Provincial People’s Hospital, Guangzhou, Guangdong, China
| | - Wenjian Wang
- Division of Nephrology, Guangdong Academy of Medical Sciences/Guangdong Provincial People’s Hospital, Guangzhou, Guangdong, China
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- *Correspondence: Xiaokai Mo, ; Yuxian Zhang, ; Wenjian Wang,
| | - Yuxian Zhang
- Department of Nuclear Medicine, ZhuJiang Hospital of Southern Medical University, Guangzhou, Guangdong, China
- *Correspondence: Xiaokai Mo, ; Yuxian Zhang, ; Wenjian Wang,
| | - Xiaokai Mo
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
- *Correspondence: Xiaokai Mo, ; Yuxian Zhang, ; Wenjian Wang,
| |
Collapse
|
6
|
Zhu L, Huang R, Li M, Fan Q, Zhao X, Wu X, Dong F. Machine Learning-Based Ultrasound Radiomics for Evaluating the Function of Transplanted Kidneys. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1441-1452. [PMID: 35599077 DOI: 10.1016/j.ultrasmedbio.2022.03.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/07/2022] [Accepted: 03/13/2022] [Indexed: 06/15/2023]
Abstract
The aim of the study described here was to investigate the value of different machine learning models based on the clinical and radiomic features of 2-D ultrasound images to evaluate post-transplant renal function (pTRF). We included 233 patients who underwent ultrasound examination after renal transplantation and divided them into the normal pTRF group (group 1) and the abnormal pTRF group (group 2) based on their estimated glomerular filtration rates. The patients with abnormal pTRF were further subdivided into the non-severe renal function impairment group (group 2A) and the severe impairment group (group 2B). The radiomic features were extracted from the 2-D ultrasound images of each case. The clinical and ultrasound image features as well as radiomic features from the training set were selected, and then five machine learning algorithms were used to construct models for evaluating pTRF. Receiver operating characteristic curves were used to evaluate the discriminatory ability of each model. A total of 19 radiomic features and one clinical feature (age) were retained for discriminating group 1 from group 2. The area under the receiver operating characteristic curve (AUC) values of the models ranged from 0.788 to 0.839 in the test set, and no significant differences were found between the models (all p values >0.05). A total of 17 radiomic features and 1 ultrasound image feature (thickness) were retained for discriminating group 2A from group 2B. The AUC values of the models ranged from 0.689 to 0.772, and no significant differences were found between the models (all p values >0.05). Machine learning models based on clinical and ultrasound image features, as well as radiomics features, from 2-D ultrasound images can be used to evaluate pTRF.
Collapse
Affiliation(s)
- Lili Zhu
- Department of Ultrasound, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
| | - Renjun Huang
- Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
| | - Ming Li
- Department of Nephrology, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
| | - Qingmin Fan
- Department of Ultrasound, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
| | - Xiaojun Zhao
- Department of Urology, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
| | - Xiaofeng Wu
- Department of Ultrasound, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China
| | - Fenglin Dong
- Department of Ultrasound, First Affiliated Hospital of Soochow University, Suzhou City, Jiangsu Province, China.
| |
Collapse
|